The present disclosure relates to an image-based method and system for detecting vehicle occupancy violations for managed lane enforcement. The disclosure finds application in traffic management. However, it is appreciated that the present exemplary embodiments are also amendable to other like applications.
One mechanism used to reduce congestion on busy commuter highway corridors is to impose limits on the use of a lane. For example, certain occupancy rules may be required for vehicles to use a managed lane. Examples of managed lanes include High Occupancy Vehicle (HOV) lanes and High Occupancy Tolling (HOT) lanes. In HOV lanes, vehicles are required to carry at least two occupants in order to use the lane. In HOT lanes, however, single occupant vehicles can pay a toll to use the lane. Several advantages result from the use of managed lanes. Vehicles can reach destinations in a timelier manner and the Department of Transportation (DOT) can generate revenue for roadway maintenance.
One challenge associated with managed lanes is enforcement against violations. Traditionally, enforcement of violations is performed by traffic law enforcement officers that make traffic stops in response to visual detections. However, this method is costly in labor required for observation and in fines lost from undetected violations. For example, certain studies report an estimated ninety percent of violators can escape detection, which results in lost revenue, estimated in the billions, for the DOT. Furthermore, it exposes officers to the dangers of oncoming traffic while making the traffic stop.
In an effort to reduce costs and improve efficiency, municipalities are exploring the use of new technologies for automating enforcement methods. In one example, radio frequency identification (RFID) transponders are used to assess tolls in HOT lanes. These transponders send signals based on the position of a switch located in the vehicle. The switch position indicates whether the vehicle currently contains a single occupant or multiple occupants. One drawback with this automated method is that it relies on the compliance of a driver. Because the switch is manually set by the driver, compliance becomes voluntary.
Another example of a conventional automated enforcement method performs object recognition by searching for objects based on image content assumptions. This method is based on the assumption that different objects within the image, such as faces, seats, and seat belts, are visible to the camera. Therefore, parts of the image are analyzed to determine a location of the objects and appearance characteristics, such as color, size, texture, and shape, etc., of the objects. In one example, the appearance characteristic can include spectral features, which can be extracted for detecting pixels belonging to the skin of an occupant. The extraction of the appearance characteristics can be performed via a feature representation of the object. The objects in the image that have characteristics that match a reference object are associated as being the same as the reference object. In other words, the object is labeled as being an occupant or a seat, etc.
One problem associated with conventional object detection is that variations in the captured image can result in incorrect classifications. For example, the object recognition approach may incorrectly classify an image as having a single-occupant when a passenger is leaning forward. In this instance, shown in FIGS. 1A and 18, the appearance characteristics that are extracted from the image match those of a seat and fail to match reference features corresponding to a face. FIGS. 2A and 2B show another variation in which an occupant is facing sideways when the reference object used in the object recognition approach is adapted to detect forward-facing occupants. In this instance, the vehicle is incorrectly classified as having a single-occupant because the driver is only identified. In yet another example, shown in FIGS. 3A and 3B, the object recognition approach can incorrectly classify a vehicle as having a single occupant when it fails to identify, as an object, a rear-seated passenger sitting behind the driver.
Accordingly, there is a need for an improved and more accurate automatic or semi-automatic enforcement of managed lanes. A system and a method is needed that classifies an entire windshield and/or cabin region instead of searching for specific objects situated inside parts of the image using appearance and spectral features. More specifically, there is needed an approach that makes no assumptions about the content of images in advance of the process.